| Fault diagnosis technology is a general term for the detection of abnormal state,abnormal state cause identification and abnormal state prediction of mechanical equipment.In this paper,the colliery scraper conveyor driver as the research object,through fault diagnosis technology to study the accurate detection,extraction and processing of fault data.Scraper conveyor as important coupling of working face with the outside world,in the process of coal mining occupies very important position,with the development of science and technology,the scraper conveyer has been developed to heavy duty,automation,because of the bad coal mine working environment,working face scraper conveyor,tonnage installation procedure is multifarious,long transportation line system is complex,scraper conveyor prolonged impact and high loads,the wastage of the internal components faster card chain,the chain scission,take off a tooth,bottom chain fall failure often happen,such as the working efficiency of moment threatening.Taking the sgz1000/2 ×525 middle double-chain front scraper conveyor of 250102-2 fully mechanized mining face of Huating coal mine as the research object,this paper mainly studied the fault diagnosis technology of the scraper conveyor and the fault signal processing of the frequency converter supporting the scraper conveyor.The research results are as follows:(1)analyze the internal structure of the scraper conveyor,which is composed of the head drive part,the tail drive part and the middle part.It is concluded from the fault analysis that the fault mainly occurs in the reducer,motor,scraper chain and sprocket shaft group.The frequency converter fault mainly occurs in the main circuit and control circuit,and lists the common faults of the scraper conveyor frequency converter.(2)the limitations of first-generation wavelet transform in fault diagnosis are discussed,and the advantages of second-generation wavelet transform in constructing waveform matching with signal are discussed.MALAB software was used to decompose and reconstruct the waveform data of bearing inner ring fault and gear wear fault,and the wavelet energy spectrum was obtained to determine the fault frequency range.(3)MATALB was used to decompose the fault signal of the scraper conveyor’s converter by wavelet,and the low-frequency energy value of each phase of the converter’s three-phase current was extracted.After normalization,three fault related feature vectors were obtained.(4)the application of fuzzy clustering theory is researched based on fuzzy theory and fuzzy c-means clustering algorithm(FCM)in fault analysis of scraper conveyor.(5)select the fault monitoring points of the reducer and the motor,collect the fault data of the monitoring points,extract 200 groups of data from the two observation points,and normalize the data.FCM algorithm of MATLAB was used to process 200 groups of data,and a more intuitive FCM clustering result graph was obtained,and a better clustering effect was achieved.In this paper,coal mine scraper conveyor sgz1000/2×525 as the research object,in view of the conveyor’s multiple faults,using the second generation of wavelet transform to obtain the wavelet energy spectrum to determine the fault frequency range;The application of fuzzy clustering theory and fuzzy c-means clustering algorithm(FCM)in fault analysis of scraper conveyor is introduced.A more intuitive clustering result graph of FCM is obtained and a better clustering effect is obtained.The research results can effectively reduce the transport failure and provide a basis for the safe and efficient production of coal mine. |
